class_docstr: A regularizer that applies a L2 regularization penalty.
  
  The L2 regularization penalty is computed as:
  `loss = l2 * reduce_sum(square(x))`
  
  L2 may be passed to a layer as a string identifier:
  
  >>> dense = Dense(3, kernel_regularizer='l2')
  
  In this case, the default value used is `l2=0.01`.
  
  Arguments:
      l2: float, L2 regularization factor.
code: def __call__(self, x):
      return self.l2 * ops.sum(ops.square(x))
  
imports: import math
  from keras import ops
  from keras.api_export import keras_export
  from keras.utils.numerical_utils import normalize
label_desc: Use image node_id 2 for calling the L2 obj's underlying member method code with example usage: obj.__call__(x) and returns: unknown